Desaturation severity prediction and alarm management

ABSTRACT

Implementations described herein disclose a method of classifying oxygen level desaturation events. In one implementation, the method includes receiving input signal sequences, the input signals indicative of a physiological condition of a patient, generating an input sequence of oxygen saturation levels based on the input signal sequence, comparing the input sequence of oxygen saturation levels to a desaturation alarm threshold to determine a desaturation event, generating an input feature matrix based on at least one of the input signal sequences and the input sequence of oxygen saturation levels, and classifying based on the input feature matrix, using a neural network, the desaturation event being a severe desaturation event (SDE) or a non-severe desaturation event (non-SDE).

BACKGROUND

Biomedical monitoring devices such as pulse oximeters, glucose sensors,electrocardiograms, capnometers, fetal monitors, electromyograms,electroencephalograms, ultrasounds, etc., may provide lagging indicatorsof physiological phenomena. In other words, these devices generallyprovide a signal or a series of signals that are indicative of a patientcondition that has already occurred. For example, a pulse oximeter is asmall, clip-like device that attaches to a body part, like toes or anearlobe. It's most commonly put on a finger to measure how well apatient's heart is pumping oxygen through the body by determining theoxygen content of arterial blood. However, while the oxygen levelprovides an indication of various conditions such as hypoxemia, lowcardiac output, tissue perfusion issues, the oxygen saturation leveldetermined by a pulse oximeter is a lagging indicator that may beindicative of the physiological condition causing such saturation level.

SUMMARY

Implementations described herein disclose a method of classifying oxygenlevel desaturation events. In one implementation, the method includesreceiving input signal sequences, the input signals indicative of aphysiological condition of a patient, generating an input sequence ofoxygen saturation levels based on the input signal sequence, comparingthe input sequence of oxygen saturation levels to a desaturation alarmthreshold to determine a desaturation event, generating input featurematrices based on at least one of the input signal sequences and theinput sequence of oxygen saturation levels, and classifying based on theinput feature matrix, using a neural network, the desaturation eventbeing a severe desaturation event (SDE) or a non-severe desaturationevent (non-SDE).

In an alternative implementation, classifying the desaturation eventfurther includes inputting the input feature matrix to the neuralnetwork, predicting a length of the desaturation event using the neuralnetwork, and classifying the desaturation event based on the predictedlength of the desaturation event. Alternatively, classifying thedesaturation event further includes inputting the input feature matrixto the neural network, predicting a depth of the desaturation eventusing the neural network, and classifying the desaturation event basedon the predicted length of the desaturation event. Alternatively, themethod further includes reducing an alarm delay in response toclassifying the desaturation event being an SDE.

In an alternative implementation, the method further includes increasingan alarm delay in response to classifying the desaturation event being anon-SDE. Alternatively, the method further includes generating, based onthe input feature matrix and using the neural network, probabilityassociated with the desaturation event being SDE or non-SDE. Yetalternatively, the method further includes comparing the probabilityassociated with the desaturation event being SDE or non-SDE with athreshold probability and adjusting the alarm delay in response to thecomparison. Alternatively, adjusting the alarm delay further comprisesadjusting the alarm delay based on the alarm delay as a non-linearfunction of the probability associated with the desaturation event. Inone implementation, the neural network is at least one of aconvolutional neural network (CNN) and a long short-term memory (LSTM)neural network. Alternatively, the input feature matrix is one of amatrix of maximum slopes of PPG pulses, a matrix of steepness of PPGpulses, a matrix of normalized amplitudes of PPG pulses, a matrix ofmaximum curvatures of PPG pulses, and a matrix of maximum negativeslopes before dicrotic notches of PPG pulses.

In a computing environment, a method performed at least in part on atleast one processor, the method including receiving input signalsequences, the input signals indicative of a physiological condition ofa patient, generating an input sequence of oxygen saturation levelsbased on the input signal sequences, comparing the input sequence ofoxygen saturation levels to a desaturation alarm threshold to determinea desaturation event, generating an input feature matrix based on atleast one of the input signal sequences and the input sequence of oxygensaturation levels, classifying, based on the input feature matrix andusing a neural network, the desaturation event being a severedesaturation event (SDE) or a non-severe desaturation event (non-SDE),and adjusting an alarm delay in response to classifying the desaturationevent being an SDE or a non-SDE.

A physical article of manufacture including one or more tangiblecomputer-readable storage media, encoding computer-executableinstructions for executing on a computer system a computer process toprovide an automated connection to a collaboration event for a computingdevice, the computer process including receiving input signal sequences,the input signals indicative of a physiological condition of a patient,generating an input sequence of oxygen saturation levels based on theinput signal sequences, comparing the input sequence of oxygensaturation levels to a desaturation alarm threshold to determine adesaturation event, generating an input feature matrix based on at leastone of the input signal sequences and the input sequence of oxygensaturation levels, classifying, based on the input feature matrix andusing a neural network, the desaturation event being a severedesaturation event (SDE) or a non-severe desaturation event (non-SDE),and adjusting an alarm delay in response to classifying the desaturationevent being an SDE or a non-SDE.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Other implementations are also described and recited herein.

BRIEF DESCRIPTIONS OF THE DRAWINGS

A further understanding of the nature and advantages of the presenttechnology may be realized by reference to the figures, which aredescribed in the remaining portion of the specification.

FIG. 1 illustrates an example schematic view of oxygen leveldesaturation severity prediction system for a patient as disclosedherein.

FIG. 2 illustrates example operations for managing alarm delay based onoxygen level desaturation severity predictions.

FIG. 3 illustrates example operations for managing alarm delay based onprobabilities of oxygen level desaturation severity.

FIG. 4 illustrates an example graph indicating alarm delay based onprobability associated with classification of an oxygen leveldesaturation event.

FIG. 5 illustrates an example long short-term memory (LSTM) architectureused as the classifier for the oxygen level desaturation severityprediction system disclosed herein.

FIG. 6 illustrates an example neural network architecture used as theclassifier for the oxygen level desaturation severity prediction systemdisclosed herein.

FIG. 7 illustrates an example computing system that may be useful inimplementing the described technology.

DETAILED DESCRIPTIONS

Hypoxemia is a condition that indicates lower than normal concentrationof oxygen levels in arterial blood of a patient. Hypoxemia may result inhypoxia or hypoxic condition for the patient, characterized byinadequate oxygen content in patient tissues. Pulse oximeters may beused to measure the oxygen content of arterial blood to indicateexisting hypoxia condition as well as to predict impending hypoxia. Aclinician may want to be alerted when oxygen saturation level inarterial blood dips below a threshold, indicating an oxygen leveldesaturation event.

However, in some circumstances such a desaturation event may be trivialin that the oxygen saturation level may dip below the threshold only fora short period—such as a few seconds. Such short desaturation event maynot warrant generating an alarm to notify the clinician. Similarly, inother circumstances, the desaturation event may be trivial in the sensethat even when it is for a longer time period, the saturation levels maynot have dipped significantly below the threshold, in which case, evenif such event may warrant an alarm, the alarm generation may be delayed.

On the other hand, the desaturation event may be serious in that theoxygen saturation level may dip below the threshold for a prolongedperiod and/or significantly below the threshold level. The technologydisclosed herein provides a method for alarm management based onpredicted severity of oxygen level desaturation events. Specifically,the method and system disclosed herein may use artificial intelligence(AI) based alarm management where neural network (NN) models may be usedto predict the probability of a severe desaturation event (SDE) or anon-severe desaturation event (non-SDE).

For example, a deep learning model may be trained to determine theseverity of a desaturation event as the desaturation threshold isreached. The severity may, for example, be determined by the predicteddepth of the desaturation event, the predicted time spent below thesaturation threshold, or a combination of both.

FIG. 1 illustrates an example schematic view of oxygen leveldesaturation severity prediction system 100 for a patient 102. A pulseoximeter 104 may be used to measure the oxygen saturation (SpO₂) levelin the patient. For example, the pulse oximeter 104 may be attached tothe patient's thumb. The pulse oximeter 104 may be communicativelyconnected to a computing system 120. For example, the pulse oximeter 104may be connected to the computing system 120 wirelessly and it may senda sequence of input signals 110 measured by the oximeter 104 over aperiod of time. For example, such input signal sequence 110 may becommunicated at every second. In one implementation, the input signalsequence 110 may include photoplethysmographic (PPG) signals such as ared signal 110 a, an infrared signal 110 b, etc. The pulse oximeter 104may also use the values of the red signal 110 a and the infrared signal110 b, to generate values of oxygen saturation levels (the SpO₂ levels).A sequence of such SpO₂ levels generated by the pulse oximeter 104 isillustrated by a sequence of oxygen saturation levels 170.

The computing device 120 may be a computing system that includes amicroprocessor 122, a memory 124, and various other components. Anexample of such a computing system 120 is disclosed in FIG. 7 below. Ina method disclosed herein, the memory 124 may be used to store thesequence of input signals 110 generated by the pulse oximeter 104, thesequence of SpO₂ levels generated by the pulse oximeter 104, as well asvarious feature matrices 140 generated based on the sequence of inputsignals 110. For example, a feature matrix generator 130 may be used togenerate the feature matrices 140 based on the sequence of input signals110.

Such feature matrices 140 may include a feature matrix that representsmaximum slopes of PPG pulse, the steepness of a PPG signal, skew of thePPG signal, the normalized amplitude of PPG pulse, maximum curvature ofthe PPG pulses, etc. For example, the feature matrix generator 130 maydetermine the maximum positive slope for a predetermined number of PPGpulses and generate a matrix of such maximum positive slopes.Alternatively, the feature matrix generator 130 may determine themaximum negative slope before dicrotic notches for a predeterminednumber of PPG pulses and generate a matrix of such maximum negativeslopes. Another example of a feature matrix may be a feature matrix ofmaximum peak-to-peak amplitudes for a predetermined number of PPGpulses. In an alternative implementation, one or more feature matricesbased on combination of the feature matrices discussed above may also begenerated. In another alternative implementation, the feature matrices140 may also include a matrix of the original PPG signal itself.

A graph 150 discloses oxygen saturation levels 170 in percentagesagainst time. A saturation monitor module 132 monitors the oxygensaturation levels 170 as compared to a desaturation alarm threshold 152.For example, the desaturation alarm threshold 152 may be set to be at90% oxygen saturation. As indicated in graph 150, the oxygen saturationlevels 170 may cross the desaturation alarm threshold 152 at point A 160to indicate onset of a desaturation event. Such desaturation event maybe an SDE as indicated by a line 174 or may be a non-SDE as indicated bya line 172. For example, line 174 is considered to indicate an SDE dueto the amount of the time L1 the oxygen saturation level is below thedesaturation alarm threshold 152. Alternatively, line 174 may also beconsidered to indicate an SDE due to the significant depth D1 of theoxygen saturation level below the desaturation alarm threshold 152. Onthe other hand, a line 172 may be considered a non-SDE due to theshorter amount of time L2 for which the oxygen saturation level is belowthe desaturation alarm threshold 152 and/or due to the lesser depth D2of the oxygen saturation level below the desaturation alarm threshold152.

Furthermore, line 174 also indicates the oxygen saturation levelsdipping below a severity threshold 154. For example, the severitythreshold may be at 75% oxygen saturation, thus indicating an SDE.Compared to that line 172 does not dip below the severity threshold 154,which may be indicative of a non-SDE. As discussed below, the oxygenlevel desaturation severity prediction system 100 allows predicting theseverity level of the desaturation event in response to the oxygensaturation levels 170 crossing the alarm threshold 152 using AI.

The memory 124 may also store instructions of a neural network basedclassifier module 134 that can be executed using the micro-processor122. The classifier module 134 may be used to analyze sequences of inputsignals 110 generated by the pulse oximeter 104, the sequence of SpO₂levels generated by the pulse oximeter 104, as well as various featurematrices 140 generated based on the sequences of input signals110—together these sequences are referred to herein as the classifierinput sequences. In one implementation the classifier module 134 may bea deep neural network module that analyzes the classifier inputsequences using a long short-term memory (LSTM) based layers to predictseverity level of desaturation events based on analysis of the variousclassifier input sequences. Alternatively, the classifier module 134 maybe a convolutional neural network (CNN), a recurrent neural network(RNN), etc.

The classifier module 134 may be configured to predict severity level ofdesaturation events based on analysis of the various classifier inputsequences. For example, the classifier module 134 may predict the lengthof the desaturation event and/or the depth of the desaturation eventover a predetermined future time period, starting at point A 160, todetermine whether the desaturation event is going to be an SDE or anon-SDE. In one implementation, the classifier module 134 outputs theprobability of the desaturation event is going to be an SDE or anon-SDE.

Such probability may be used to manage an alarm to a clinician.Specifically, the memory 124 may also have an alarm manager module 136that is configured to manage generation of alarm based as thedesaturation threshold is reached based on classification of thedesaturation event being an SDE or a non-SDE. For example, the alarmmanager module 136 may compare the probability output generated by theclassifier module 134 to a threshold probability P_(thresh) to changethe length of time before an alarm is generated.

The P_(thresh) may be set by a user of the oxygen level desaturationseverity prediction system 100. Similarly, a default alarm delay timeperiod may also be set by the user. If the alarm manager module 136determines the probability of an SDE, output by the classifier module134, is below the P_(thresh), it leaves the alarm delay time period tothe default value. However, if the alarm manager module 136 determinesthe probability of an SDE, output by the classifier module 134, is abovethe P_(thresh), it alters the alarm delay time period to below thedefault value.

As an example, the alarm delay may be set by default at 5, 10, or 20seconds after crossing the desaturation alarm threshold 152. Then for aprobability of an SDE>P_(Thresh) the alarm delay may be reduced to zeroor near zero. Alternatively, in such a case, if the alarm wereoriginally set at 20 seconds delay, the delay may be reduced to 10 or 5seconds. On the other hand, if a high probability of anon-SDE>P_(Thresh) is exhibited, the alarm delay could be increased tofor example 30, 60, 90 seconds or more. In one implementation, theprobability of the desaturation event being SDE or non-SDE may beobtained from the classifier module 134 prior to final classification ofthe desaturation event as being SDE or non-SDE. Furthermore, theprobability of the desaturation event being SDE or non-SDE may be linkedto the alarm delay through a non-linear function as further discussedbelow in FIG. 4.

In one implementation, the alarm generation module 134 may provide analarm management user interface (UI) to a user that allows the user tocontrol how quickly the alarm is generated after the saturation levelscrossing the desaturation alarm threshold 152. For example, such alarmmanagement UI may allow the user to set the slope of the non-linearfunction linking the probability of the desaturation event being SDE ornon-SDE to the alarm delay.

FIG. 2 illustrates example operations 200 for managing alarm delay basedon oxygen level desaturation severity predictions. Specifically, one ormore of the operations 200 may be implemented by computer executableinstructions stored in the alarm manager module 136. An operation 202determines if the oxygen saturation levels have crossed the desaturationalarm threshold to dip below the desaturation alarm threshold. If theoxygen saturation levels have not crossed the desaturation alarmthreshold to dip below the desaturation alarm threshold, an operation204 continues monitoring the oxygen saturation levels compared to thedesaturation alarm threshold.

If the oxygen saturation levels have crossed the desaturation alarmthreshold to dip below the desaturation alarm threshold, an operation206 reviews the predicted classification of the desaturation event. Forexample, the operation 206 may review such classification from a neuralnetwork based classifier such as the classifiers disclosed below inFIGS. 5 and 6. If the desaturation event is classified as being an SDE,an operation 208 reduces the alarm delay so that the alarm may begenerated relatively quickly after the saturation levels crossing thedesaturation alarm threshold. On the other hand, if the desaturationevent is classified as being a non-SDE, an operation 210 increases thealarm delay so that the alarm may be generated relatively later afterthe saturation levels crossing the desaturation alarm threshold.

FIG. 3 illustrates example operations 300 for managing alarm delay basedon probability of oxygen level desaturation severity. Specifically, oneor more of the operations 300 may be implemented by computer executableinstructions stored in the alarm manager module 136. An operation 302determines if the oxygen saturation levels have crossed the desaturationalarm threshold to dip below the desaturation alarm threshold. If theoxygen saturation levels have not crossed the desaturation alarmthreshold to dip below the desaturation alarm threshold, an operation304 continues monitoring the oxygen saturation levels compared to thedesaturation alarm threshold.

If the oxygen saturation levels have crossed the desaturation alarmthreshold to dip below the desaturation alarm threshold, an operation306 reviews the predicted classification of the desaturation event. Forexample, the operation 306 may review such classification from a neuralnetwork based classifier such as the classifiers disclosed below inFIGS. 5 and 6. If the desaturation event is classified as being an SDE,an operation 308 compares the probability P_(SDE) associated with theSDE with the P_(thresh). If P_(SDE)>P_(thresh), an operation 312decreases the alarm delay, resulting an alarm generation relativelysooner after the saturation levels cross the desaturation alarmthreshold. On the other hand, if P_(SDE)<=P_(thresh), an operation 316uses the default alarm delay.

On the other hand, if the desaturation event is classified as being anon-SDE, an operation 310 compares the probability P_(non-SDE)associated with the non-SDE with the P_(thresh). IfP_(non-SDE)>P_(thresh), an operation 314 increases the alarm delay,resulting an alarm generation relatively later after the saturationlevels cross the desaturation alarm threshold. On the other hand, ifP_(non-SDE)<=P_(thresh), the operation 316 uses the default alarm delay.

FIG. 4 illustrates an example graph 400 indicating alarm delay based onprobability associated with classification of an oxygen leveldesaturation event. Specifically, the graph 400 illustrates how theprobability (P) associated with classification of an oxygen leveldesaturation event itself may be linked to the delay period through alinear or nonlinear function. Here the alarm delay period remains at thedefault level 406 until P reaches a threshold value, which is set to beat 0.8 in this illustration. Thereafter, if a severe event is predicted,thus P being P_(SDE), the alarm delay period is reduced to zero as alinear function of the probability P as indicated by 404. On the otherhand, if a non-severe event is predicted, thus P being P_(non-SDE), thealarm delay period is increased to a maximum value as a linear functionof the probability P as indicated by 402. The functions 402 and 404depicted in FIG. 4 are piecewise linear functions. However, thesefunctions may also be continuous and/or non-linear. Furthermore, thethreshold for the delay period default (0.8) may be altered to anynumber between 0.5 and 1.0.

In the above disclosed implementation, it is assumed that the predictionof the desaturation event is made as the saturation level crosses thedesaturation alarm threshold. However, alternatively, the prediction ofthe desaturation event and its associated probability may be calculatedcontinuously subsequent to the saturation level crossing thedesaturation alarm threshold. This results in continuous updating of theprediction and resulting action regarding the alarm generation. Forexample, a non-SDE classification at threshold crossing may be updatedto an SDE classification after a time period below the threshold, inwhich case the alarm could be sounded immediately.

Furthermore, the above illustrations use a fixed criterion for an SDE asa second fixed threshold below the desaturation alarm threshold (e.g.90%). This second fixed threshold may be set at, e.g., 15 saturationpercentage points below the desaturation alarm threshold. However, analternative implementation may use alternative criteria for severityincluding a drop below a baseline level for the patient. For example, apatient may have a baseline level of 87% and a severe desaturation maybe defined as a 15% drop below this level (e.g. at 72% saturation).

The AI method used by the classifier may consist of, for example, a deeplearning model, including a convolutional neural network (CNN) or along-short term memory network (LSTM). FIG. 5 illustrates an exampledeep learning LSTM network 500 used as the classifier for the oxygenlevel desaturation severity prediction system disclosed herein.Specifically, one or more layers of the LTSM architecture 500 may beimplemented by computer executable instructions stored in the classifiermodule 134. The LSTM network 500 may include multiple layers 502-518.The LSTM network 500 may be trained on historic data sets where bothsevere and non-severe desaturation events are present.

The LSTM network 500 may be trained on the original PPG signals (red andinfrared, such as the signal 110 a, 110 b illustrated in FIG. 1), thecalculated SpO₂ values by the oximeter 102, the heart rate calculated bythe oximeter 102, and/or other feature matrices 140 derived from the PPGsignal, etc. An example PPG pulse 530 discloses various PPG pulseparameters that may be used to generate input features for the LSTMnetwork 500. For example, the feature matrices 140 derived from the PPGsignal may include: skew of the pulses, amplitude of the pulses,normalized amplitude of the pulses, maximum slope of the PPG pulse 530,the maximum curvature of the PPG pulse 530, the location of maximumslope, curvature, and other morphological parameters derived from thePPG pulse 530.

In the illustrated implementation of the LSTM network 500, the classoutput layer 518 may output the classification of the desaturation eventbeing SDE or non-SDE. However, an intermediate layer, such as thesoftmax layer may output the probabilities associated with thedesaturation events being SDE or non-SDE.

FIG. 6 illustrates an example convolutional neural network (CNN) 600used as the classifier for the oxygen level desaturation severityprediction system disclosed herein. Specifically, one or more layers ofthe CNN 600 may be implemented by computer executable instructionsstored in the classifier module 134. The CNN 600 may be a residualneural network (ResNet) that builds on constructs known from pyramidalcells by utilizing shortcuts to jump over some layers. The CNN 600 isillustrated as made of repeating blocks 610 a, 610 b, which may berepeated an arbitrary number of times. Furthermore, specific parametersof each convolution, such as stride (controlling how a filter convolvesaround an input volume), padding (adding zeros to input matrix), etc.,can be tuned. In the illustrated implementation, there are two (2) fullyconnected layers between the ReLu layer 620 and the softmax payer 630.However, alternative implementations may include, n (n>1) number offully connected layers between the ReLu layer 620 and the softmax payer630.

FIG. 7 illustrates an example system 700 that may be useful inimplementing the described technology for providing attestable anddestructible device identity. The example hardware and operatingenvironment of FIG. 7 for implementing the described technology includesa computing device, such as a general-purpose computing device in theform of a computer 20, a mobile telephone, a personal data assistant(PDA), a tablet, smart watch, gaming remote, or other type of computingdevice. In the implementation of FIG. 7, for example, the computer 20includes a processing unit 21, a system memory 22, and a system bus 23that operatively couples various system components including the systemmemory to the processing unit 21. There may be only one or there may bemore than one processing unit 21, such that the processor of thecomputer 20 comprises a single central-processing unit (CPU), or aplurality of processing units, commonly referred to as a parallelprocessing environment. The computer 20 may be a conventional computer,a distributed computer, or any other type of computer; theimplementations are not so limited.

The system bus 23 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, aswitched fabric, point-to-point connections, and a local bus using anyof a variety of bus architectures. The system memory may also bereferred to as simply the memory, and includes read only memory (ROM) 24and random-access memory (RAM) 25. A basic input/output system (BIOS)26, containing the basic routines that help to transfer informationbetween elements within the computer 20, such as during start-up, isstored in ROM 24. The computer 20 further includes a hard disk drive 27for reading from and writing to a hard disk, not shown, a magnetic diskdrive 28 for reading from or writing to a removable magnetic disk 29,and an optical disk drive 30 for reading from or writing to a removableoptical disk 31 such as a CD ROM, DVD, or other storage media.

The hard disk drive 27, magnetic disk drive 28, and optical disk drive30 are connected to the system bus 23 by a hard disk drive interface 32,a magnetic disk drive interface 33, and an optical disk drive interface34, respectively. The drives and their associated tangiblecomputer-readable media provide non-volatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computer 20. It should be appreciated by thoseskilled in the art that any type of tangible computer-readable media maybe used in the example operating environment.

A number of program modules may be stored on the hard disk drive 27,magnetic disk 28, optical disk 30, ROM 24, or RAM 25, including anoperating system 35, one or more application programs 36, other programmodules 37, and program data 38. A user may generate reminders on thepersonal computer 20 through input devices such as a keyboard 40 andpointing device 42. Other input devices (not shown) may include amicrophone (e.g., for voice input), a camera (e.g., for a natural userinterface (NUI)), a joystick, a game pad, a satellite dish, a scanner,or the like. These and other input devices are often connected to theprocessing unit 21 through a serial port interface 46 that is coupled tothe system bus 23, but may be connected by other interfaces, such as aparallel port, game port, or a universal serial bus (USB) (not shown). Amonitor 47 or other type of display device is also connected to thesystem bus 23 via an interface, such as a video adapter 48. In additionto the monitor, computers typically include other peripheral outputdevices (not shown), such as speakers and printers.

The computer 20 may operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer 49.These logical connections are achieved by a communication device coupledto or a part of the computer 20; the implementations are not limited toa particular type of communications device. The remote computer 49 maybe another computer, a server, a router, a network PC, a client, a peerdevice or other common network node, and typically includes many or allof the elements described above relative to the computer 20. The logicalconnections depicted in FIG. 7 include a local-area network (LAN) 51 anda wide-area network (WAN) 52. Such networking environments arecommonplace in office networks, enterprise-wide computer networks,intranets and the Internet, which are all types of networks.

When used in a LAN-networking environment, the computer 20 is connectedto the local network 51 through a network interface or adapter 53, whichis one type of communications device. When used in a WAN-networkingenvironment, the computer 20 typically includes a modem 54, a networkadapter, a type of communications device, or any other type ofcommunications device for establishing communications over the wide areanetwork 52. The modem 54, which may be internal or external, isconnected to the system bus 23 via the serial port interface 46. In anetworked environment, program engines depicted relative to the personalcomputer 20, or portions thereof, may be stored in the remote memorystorage device. It is appreciated that the network connections shown areexamples and other means of communications devices for establishing acommunications link between the computers may be used.

In an example implementation, software or firmware instructions forproviding attestable and destructible device identity may be stored inmemory 22 and/or storage devices 29 or 31 and processed by theprocessing unit 21. One or more datastores disclosed herein may bestored in memory 22 and/or storage devices 29 or 31 as persistentdatastores. For example, an SpO₂ desaturation severity prediction system702 (illustrated within the personal computer 20) may be implemented onthe computer 20 (alternatively, the SpO₂ desaturation severityprediction system 702 may be implemented on a server or in a cloudenvironment). The SpO₂ desaturation severity prediction system 702 mayutilize one of more of the processing unit 21, the memory 22, the systembus 23, and other components of the personal computer 20.

In contrast to tangible computer-readable storage media, intangiblecomputer-readable communication signals may embody computer readableinstructions, data structures, program modules or other data resident ina modulated data signal, such as a carrier wave or other signaltransport mechanism. The term “modulated data signal” means a signalthat has one or more of its characteristics set or changed in such amanner as to encode information in the signal. By way of example, andnot limitation, intangible communication signals include wired mediasuch as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless communication methods.

The implementations described herein are implemented as logical steps inone or more computer systems. The logical operations may be implemented(1) as a sequence of processor-implemented steps executing in one ormore computer systems and (2) as interconnected machine or circuitmodules within one or more computer systems. The implementation is amatter of choice, dependent on the performance requirements of thecomputer system being utilized. Accordingly, the logical operationsmaking up the implementations described herein are referred to variouslyas operations, steps, objects, or modules. Furthermore, it should beunderstood that logical operations may be performed in any order, unlessexplicitly claimed otherwise or a specific order is inherentlynecessitated by the claim language.

The above specification, examples, and data provide a completedescription of the structure and use of exemplary embodiments of theinvention. Since many implementations of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims hereinafter appended. Furthermore,structural features of the different embodiments may be combined in yetanother implementation without departing from the recited claims.

What is claimed is:
 1. A method, of classifying oxygen leveldesaturation events, comprising: receiving input signal sequences, theinput signals indicative of a physiological condition of a patient;generating an input sequence of oxygen saturation levels based on theinput signal sequences; comparing the input sequence of oxygensaturation levels to a desaturation alarm threshold to determine adesaturation event; generating an input feature matrix based on at leastone of the input signal sequences and the input sequence of oxygensaturation levels; and classifying, based on the input feature matrixand using a neural network, the desaturation event being a severedesaturation event (SDE) or a non-severe desaturation event (non-SDE).2. The method of claim 1, wherein classifying the desaturation eventfurther comprising: inputting the input feature matrix to the neuralnetwork; predicting a length of the desaturation event using the neuralnetwork; and classifying the desaturation event based on the predictedlength of the desaturation event.
 3. The method of claim 1, whereinclassifying the desaturation event further comprising: inputting theinput feature matrix to the neural network; predicting a depth of thedesaturation event using the neural network; and classifying thedesaturation event based on the predicted length of the desaturationevent.
 4. The method of claim 1, further comprising reducing an alarmdelay in response to classifying the desaturation event being an SDE. 5.The method of claim 1, further comprising increasing an alarm delay inresponse to classifying the desaturation event being a non-SDE.
 6. Themethod of claim 1, further comprising generating, based on the inputfeature matrix and using the neural network, probability associated withthe desaturation event being SDE or non-SDE.
 7. The method of claim 6,further comprising: comparing the probability associated with thedesaturation event being SDE or non-SDE with a threshold probability;and adjusting the alarm delay in response to the comparison.
 8. Themethod of claim 7, wherein adjusting the alarm delay further comprisesadjusting the alarm delay based on the alarm delay as a non-linearfunction of the probability associated with the desaturation event. 9.The method of claim 1, wherein the neural network is at least one of aconvolutional neural network (CNN) and a long short-term memory (LSTM)neural network.
 10. The method of claim 1, wherein the input featurematrix is one of a matrix of maximum slopes of PPG pulses, a matrix ofsteepness of PPG pulses, a matrix of normalized amplitudes of PPGpulses, a matrix of maximum curvatures of PPG pulses, and a matrix ofmaximum negative slopes before dicrotic notches of PPG pulses.
 11. In acomputing environment, a method performed at least in part on at leastone processor, the method comprising: receiving input signal sequences,the input signals indicative of a physiological condition of a patient;generating an input sequence of oxygen saturation levels based on theinput signal sequences; comparing the input sequence of oxygensaturation levels to a desaturation alarm threshold to determine adesaturation event; generating an input feature matrix based on at leastone of the input signal sequences and the input sequence of oxygensaturation levels; classifying, based on the input feature matrix andusing a neural network, the desaturation event being a severedesaturation event (SDE) or a non-severe desaturation event (non-SDE);and adjusting an alarm delay in response to classifying the desaturationevent being an SDE or a non-SDE.
 12. The method of claim 11, whereinclassifying the desaturation event further comprising: inputting theinput feature matrix to the neural network; predicting a length and adepth of the desaturation event using the neural network; andclassifying the desaturation event based on the predicted length and thedepth of the desaturation event.
 13. The method of claim 11, whereinadjusting the alarm delay further comprising reducing the alarm delay inresponse to classifying the desaturation event being an SDE.
 14. Themethod of claim 11, wherein adjusting the alarm delay further comprisingincreasing the alarm delay in response to classifying the desaturationevent being a non-SDE.
 15. The method of claim 11, further comprising:generating, based on the input feature matrix and using the neuralnetwork, probability associated with the desaturation event being SDE ornon-SDE; comparing the probability associated with the desaturationevent being SDE or non-SDE with a threshold probability; and adjustingthe alarm delay in response to the comparison.
 16. The method of claim15, wherein adjusting the alarm delay further comprises adjusting thealarm delay based on the alarm delay as a non-linear function of theprobability associated with the desaturation event.
 17. The method ofclaim 11, wherein the input feature matrix is one of a matrix of maximumslopes of PPG pulses, a matrix of steepness of PPG pulses, a matrix ofnormalized amplitudes of PPG pulses, a matrix of maximum curvatures ofPPG pulses, and a matrix of maximum negative slopes before dicroticnotches of PPG pulses.
 18. A physical article of manufacture includingone or more tangible computer-readable storage media, encodingcomputer-executable instructions for executing on a computer system acomputer process to provide an automated connection to a collaborationevent for a computing device, the computer process comprising: receivinginput signal sequences, the input signals indicative of a physiologicalcondition of a patient; generating an input sequence of oxygensaturation levels based on the input signal sequences; comparing theinput sequence of oxygen saturation levels to a desaturation alarmthreshold to determine a desaturation event; generating an input featurematrix based on at least one of the input signal sequences and the inputsequence of oxygen saturation levels; classifying, based on the inputfeature matrix and using a neural network, the desaturation event beinga severe desaturation event (SDE) or a non-severe desaturation event(non-SDE); and adjusting an alarm delay in response to classifying thedesaturation event being an SDE or a non-SDE.
 19. The physical articleof manufacture of claim 18, wherein the computer process furthercomprising: generating, based on the input feature matrix and using theneural network, probability associated with the desaturation event beingSDE or non-SDE; comparing the probability associated with thedesaturation event being SDE or non-SDE with a threshold probability;and adjusting the alarm delay in response to the comparison.
 20. Thephysical article of manufacture of claim 18, wherein the input featurematrix is one of a matrix of maximum slopes of PPG pulses, a matrix ofsteepness of PPG pulses, a matrix of normalized amplitudes of PPGpulses, a matrix of maximum curvatures of PPG pulses, and a matrix ofmaximum negative slopes before dicrotic notches of PPG pulses.